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COLLABORATIVE OPTIMIZATION-DRIVEN CLUSTERING FOR IMPROVED WIRELESS SENSOR NETWORK EFFICIENCY

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COLLABORATIVE OPTIMIZATION-DRIVEN CLUSTERING FOR IMPROVED WIRELESS SENSOR NETWORK EFFICIENCY

ORDINARY APPLICATION

Published

date

Filed on 12 November 2024

Abstract

Title of Invention: Collaborative Optimization-Driven Clustering for Improved Wireless Sensor Network Efficiency Field of Invention: Wireless Sensor Networks, Energy Efficiency and optimization. 7. ABSTRACT This invention introduces a collaborative optimization-driven clustering technique designed to enhance the efficiency of Wireless Sensor Networks (WSNs). By dynamically forming clusters based on real-time data, the system optimizes energy consumption and minimizes data redundancy. Sensor nodes communicate their status, including energy levels and proximity to the base station, enabling informed cluster head selection through a collaborative optimization mechanism. This ensures that nodes with optimal conditions lead data aggregation, reducing the volume of information transmitted to the base station and enhancing data accuracy. The system allows for adaptive reconfiguration of clusters, automatically initiating a new cluster head election when energy levels drop below a predefined threshold, thereby maintaining network stability. Advanced routing algorithms further optimize data transmission paths, selecting the shortest and least congested routes to conserve energy and minimize delays. Integrated energy management strategies, such as dynamic power adjustment and sleep modes for non-active nodes, prolong the operational lifespan of the network. This invention significantly improves the performance and sustainability of WSNs, making it suitable for applications in environmental monitoring, smart agriculture, healthcare, and smart city infrastructure, ultimately contributing to smarter and more efficient technological solutions.

Patent Information

Application ID202441087077
Invention FieldCOMMUNICATION
Date of Application12/11/2024
Publication Number47/2024

Inventors

NameAddressCountryNationality
Dr.R.RENUGADEVIAssociate Professor, Department of CSE, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai, Tamilnadu, India, Pin Code-602105.IndiaIndia

Applicants

NameAddressCountryNationality
SAVEETHA ENGINEERING COLLEGESAVEETHA ENGINEERING COLLEGE, SAVEETHA NAGAR THANDALAM, CHENNAI, TAMILNADU, INDIA, PIN CODE-602105.IndiaIndia

Specification

FORM 2
THE PATENTACT 1970
(ACT 39 OF 1970)
&
The Patents Rules 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

1. TITLE OF THE INVENTION
Collaborative Optimization-Driven Clustering for Improved Wireless Sensor Network Efficiency
2. APPLICANT:
SAVEETHA ENGINEERING COLLEGE
SAVEETHA NAGAR, THANDALAM,
CHENNAI - 602105, TAMILNADU.
3. PREAMBLE TO THE DESCRIPTION

The following specification particularly describes the invention and the manner in which it is to be performed.
4. DESCRIPTION
4.1 BACKGROUND OF INVENTION
Wireless Sensor Networks (WSNs) consist of numerous spatially distributed sensor nodes
that collaboratively monitor environmental conditions such as temperature, humidity, and
light. These networks are widely used in various applications, including environmental monitoring, smart agriculture, healthcare, and military surveillance. The efficient functioning of WSNs relies heavily on effective data routing and energy management, as sensor nodes typically operate on limited battery power. One of the primary challenges in WSNs is the optimization of energy consumption, which directly impacts the network's lifespan. Sensor nodes must communicate their collected data to a central base station of
sink, often leading to increased energy expenditure during data transmission. Traditional
flat routing protocols can result in uneven energy distribution among nodes, leading to premature node depletion and network failure.

Moreover, as the network scales, the need for efficient data aggregation and routing becomes more critical to minimize redundant data transmission and enhance overall network efficiency. Clustering is a widely adopted approach in WSNs to address these challenges. By grouping sensor nodes into clusters, each cluster can designate a leader or cluster head responsible for data aggregation and communication with the base station. This hierarchical structure reduces the number of transmissions and conserves energy by allowing non-cluster head nodes to enter low-power sleep modes.

However,- forming effective clusters requires careful consideration of factors such as node density, energy levels, and communication ranges. Inefficient clustering can lead to imbalanced energy consumption, reduced data accuracy, and increased latency. To enhance clustering efficiency, collaborative optimization techniques can be employed. This approach involves the joint optimization of multiple parameters, such as cluster formation, load balancing, and energy conservation, through cooperative strategies among sensor nodes. By enabling nodes to share information about their status and energy levels, collaborative optimization fosters dynamic and adaptive clustering that can respond to changing network conditions. Techniques such as genetic algorithms, particle swarm optimization, and machine learning methods have shown promise in improving clustering performance. These methods aim to minimize energy consumption while maximizing data accuracy and network lifetime.

4.2
FIELD OF INVENTION
The field of invention is primarily concerned with advancements in Wireless Sensor Networks (WSNs), which are crucial for a wide array of applications requiring real-time monitoring and data collection. WSNs consist of spatially distributed sensor nodes that gather information about physical or environmental conditions and transmit that data to a central point, typically referred to as a base station or sink. The focus of this invention is on optimizing data routing and energy efficiency through innovative clustering techniques enhanced by collaborative optimization.

Environmental Monitoring
WSNs play a pivotal role in environmental monitoring, where they are employed to collect data on atmospheric conditions, soil moisture levels, water quality, and wildlife tracking. These systems provide valuable insights for researchers and policymakers, enabling informed decision-making regarding conservation efforts and environmental management.

Smart Agriculture
In the agricultural sector, WSNs are utilized for precision farming, where sensor nodes monitor crop health, soil conditions, and climate variables. This information allows farmers to optimize irrigation, fertilization, and pest control, ultimately improving yield and resource efficiency. By employing clustering algorithms, the network can effectively manage data collection from multiple sensors while conserving energy.

Healthcare
WSNs are increasingly deployed in healthcare settings for patient monitoring. Wearable sensors can track vital signs such as heart rate, blood pressure, and glucose levels. Clustering can facilitate efficient data aggregation from multiple patients, allowing healthcare providers to analyze data in real-time while ensuring minimal energy consumption in sensor devices.

Military and Security Applications
In military and security contexts, WSNs are used for surveillance, reconnaissance, and battlefield monitoring. Sensors can detect enemy movements, monitor environmental conditions, or assess the structural integrity of buildings. Efficient clustering and routing are critical in these scenarios to ensure timely data transmission and operational effectiveness.

Smart Cities and Infrastructure
The concept of smart cities relies heavily on WSNs for managing urban infrastructure. Sensors can monitor traffic flow, waste management systems, energy consumption, and public safety. By optimizing clustering and routing, city planners can better manage resources, improve services, and enhance the quality of life for residents.

4.3 DISCUSSION OF THE RELATED ART
Wireless Sensor Networks (WSNs) have garnered significant attention due to their applications in various domains, including environmental monitoring, healthcare, and smart cities. As these networks expand, efficient data routing and energy management become paramount for maintaining performance and longevity. Recent studies have increasingly focused on collaborative optimization techniques to enhance clustering methods in WSNs,
aiming to address issues of energy efficiency and data redundancy.

Clustering is a widely adopted strategy in WSNs, where nodes are grouped to minimize
energy consumption and optimize data transmission. Recent works have introduced novel
algorithms that enhance traditional clustering methods. For example, Khanna et al. (2020)
proposed a dynamic clustering algorithm that adjusts cluster formations based on node energy
levels and communication ranges, leading to improved network lifetime and reduced energy
consumption.Similarly, Gupta et al. (2021) introduced a multi-hop clustering approach that
allows for effective load balancing among nodes. Their algorithm dynamically selects cluster
heads based on remaining energy and distance to the base station, resulting in a 25% increase
in network lifespan compared to conventional methods.

The integration of collaborative optimization techniques has shown promise in enhancing clustering performance. Raza et al. (2022) explored a cooperative clustering algorithm that
utilizes local information sharing among nodes to optimize cluster formations. This approach allows nodes to adjust their roles based on energy levels, thus promoting a more balanced energy consumption across the network. Another notable contribution is from Liu et al. (2021), who employed a particle swarm optimization (PSO) method to enhance clustering. Their study demonstrated that PSO-based clustering could significantly reduce data transmission overhead while improving energy efficiency, achieving a 30% reduction in overall energy consumption.

The application of machine learning in WSNs is becoming increasingly relevant, particularly
for optimizing clustering and routing. Chen et al. (2023) developed a reinforcement learningbased
approach to dynamically adjust cluster heads based on real-time data. Their algorithm
adapts to changes in node conditions, leading to improved data accuracy and reduced energy
expenditure. Similarly, Zhang et al. (2023) proposed a deep learning framework that predicts
node energy levels and communication patterns. By integrating this information into the
clustering process, the framework enhances the selection of cluster heads, resulting in more
efficient data routing and prolonged network lifetime.

Hybrid methods combining multiple optimization techniques are gaining traction. For instance, Kumar et al. (2022) presented a hybrid algorithm that integrates genetic algorithms and PSO for clustering in WSNs. Their results indicated significant improvements in both energy efficiency and network stability, with a reported 40% increase in overall system performance.

Moreover, Singh et al. (2023) introduced a novel clustering protocol that utilizes both fuzzy
logic and collaborative optimization. This method allows for adaptive clustering based on environmental changes, optimizing data aggregation and energy use, thus extending the network's operational lifetime.

Despite advancements, challenges remain in implementing collaborative optimization in WSNs. Issues such as dynamic node mobility, varying environmental conditions, and data security must be addressed to enhance system reliability. Future research should focus on developing adaptive algorithms that can handle real-time changes while ensuring data integrity and security. Additionally, the scalability of these algorithms in large-scale networks remains an area of concern. Continued exploration into distributed optimization techniques may provide solutions that maintain efficiency without compromising performance.

The field of collaborative optimization-driven clustering for WSNs has seen, substantial progress in recent years. By employing innovative algorithms and machine learning techniques, researchers are developing more efficient methods for energy management and data routing. These advancements are critical for enhancing the performance and longevity of WSNs across various applications. The ongoing exploration of hybrid approaches and adaptive algorithms will undoubtedly contribute to the evolution of WSN technology, paving the way for smarter and more resilient networks.

References
1. Khanna, A., Gupta, R., & Jain, S. (2020). Dynamic clustering algorithm for wireless sensor
networks. International Journal of Computer Applications, 975, 1-7.
2. Gupta, R., Singh, R., & Sharma, A. (2021). Multi-hop clustering for enhanced load
balancing in WSNs. Journal of Network and Computer Applications, 179, 102988.
3. Raza, M., Khan, A., & Iqbal, N. (2022). Cooperative clustering algorithm for wireless
sensor networks. Wireless Networks, 28(4), 1721-1735.

clustering process. These techniques improve data aggregation, minimize communication overhead, and enhance routing efficiency. The result is a substantial reduction in energy consumption while maintaining high data accuracy and network stability. This collaborative optimization-driven clustering approach has wide-ranging applications, such as in environmental monitoring, smart agriculture, healthcare, and smart city infrastructure. By enhancing the performance and sustainability of WSNs, this invention paves the way for more resilient and efficient sensor networks, contributing to smarter technological solutions across various domains.

4.5 DETAILED DESCRIPTION OF THE INVENTION
This invention focuses on enhancing the efficiency of Wireless Sensor Networks (WSNs) through a collaborative optimization-driven clustering technique. WSNs consist of numerous sensor nodes that monitor environmental conditions and transmit data to a central base station. The challenges of energy consumption, data redundancy, and network longevity necessitate innovative solutions. This invention introduces a dynamic clustering method that utilizes collaborative optimization to address these challenges effectively. Initialization Phase:

The process begins with the initialization of sensor nodes, which establish communication with one another. Each node broadcasts its status, including its energy level, location, and any data it has collected. This initial data gathering is essential for informed cluster formation.

Cluster Formation:
Using the initial information, the dynamic clustering algorithm identifies potential cluster heads. Nodes with higher energy are favored as cluster heads to prolong cluster efficiency. Nodes closer to the base station are preferred to minimize data transmission distance, in areas with high node concentration, additional clusters may be formed to balance the network load.

Cluster Head Election:
The algorithm elects cluster heads based on the criteria above. Once elected, these cluster heads assume responsibility for aggregating data from their member nodes. This hierarchical structure significantly reduces the total data sent to the base station, conserving energy.

Data Aggregation:
Member nodes transmit their data to the cluster head, which aggregates this information. Data aggregation minimizes redundancy by combining similar data from multiple sources before sending it to the base station. This process not only reduces the volume of data transmitted but also enhances the overall quality of information collected.

Collaborative Information Sharing '
Sensor nodes share information regarding their status, including energy levels and environmental
conditions. This collaborative information sharing allows nodes to make informed decisions about cluster membership and roles, promoting a balanced energy distribution throughout the network. The system continuously monitors the performance of nodes1 and cluster heads. If a cluster head's energy level drops below a predefined threshold, the system can initiate a re-election process to appoint a new cluster head. This dynamic adaptation ensures the network remains efficient and stable, even as conditions change. The
invention incorporates advanced routing algorithms that utilize the information shared among nodes to
optimize'data transmission paths. By selecting the shortest and least congested routes, the system enhances energy efficiency and minimizes delays in data delivery to the base station.

Energy Management Strategies:
The invention implements several strategies for effective energy management. For example, non-active nodes can be put into low-power sleep modes to conserve energy. Additionally, the system can adjust transmission power levels based on the distance to the receiving node, further optimizing energy consumption. An ongoing performance monitoring mechanism evaluates various metrics, such as energy consumption, data accuracy, and network longevity. This feedback informs adaptive strategies, allowing the system to adjust its clustering and routing algorithms in real-time to maintain optimal performance.

The collaborative optimization-driven clustering approach described in this invention significantly enhances the performance and efficiency of Wireless Sensor Networks. By addressing critical challenges such as energy consumption and data management, this method ensures longer operational lifespans for sensor nodes while improving data accuracy and network stability. The invention's adaptability makes it suitable for various applications, including environmental monitoring, smart agriculture, healthcare, and smart city infrastructure. Ultimately, this innovation represents a substantial advancement in WSN technology, paving the way for smarter, more efficient sensor networks.

5. CLAIMS:
1. Dynamic Cluster Formation Based on Real-Time Data
The invention claims a method for dynamically forming clusters in a Wireless Sensor Network (WSN) based on real-time information from sensor nodes. This process involves evaluating metrics such as remaining energy levels, proximity to the
base station, and node density. The dynamic nature of cluster formation allows the network to adapt to changing environmental conditions and node performance, ensuring efficient energy usage and data transmission.

2. Collaborative Optimization for Cluster Head Selection
A key feature of the invention is the collaborative optimization mechanism used for
selecting cluster heads. Sensor nodes share information about their energy levels and
communication capabilities, enabling the algorithm to identify the most suitable nodes as cluster heads. This collaborative approach ensures that cluster heads are chosen based on a holistic assessment of node conditions, thereby enhancing network stability and longevity.

3. Efficient Data Aggregation and Redundancy Minimization
The invention incorporates a data aggregation method where cluster heads combine data from member nodes before transmitting it to the base station. This aggregation minimizes data redundancy, significantly reducing the amount of information that needs to be sent, which in turn conserves energy and enhances data accuracy. The
algorithm optimizes the aggregation process by identifying and filtering out redundant
data points.

4. Adaptive Reconfiguration of Clusters
The system allows for the adaptive reconfiguration of clusters based on real-time performance monitoring. If a cluster head's energy level falls below a specified threshold, the system initiates a re-election process to appoint a new cluster head. This feature ensures that the network remains efficient and functional, even as individual node conditions fluctuate, thus prolonging the overall operational lifespan of the WSN.

5. Optimized Routing Algorithms for Energy Efficiency
The invention claims the implementation of advanced routing algorithms that optimize data transmission paths among sensor nodes. These algorithms take into account shared information regarding node status and environmental conditions to select the shortest and least congested routes for data transmission. This optimization enhances energy efficiency by reducing the overall communication distance and minimizing latency in data delivery to the base station.

6. Integrated Energy Management Strategies
The invention features integrated energy management strategies designed to maximize the operational life of sensor nodes. This includes methods for putting nonactive nodes into low-power sleep modes and dynamically adjusting transmission power levels based on the distance to receiving nodes. By optimizing energy usage across the network, the system ensures that nodes conserve power while maintaining effective communication and data transmission capabilities.

Documents

NameDate
202441087077-Form 1-121124.pdf13/11/2024
202441087077-Form 2(Title Page)-121124.pdf13/11/2024
202441087077-Form 3-121124.pdf13/11/2024
202441087077-Form 5-121124.pdf13/11/2024

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